




import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
	import bsa
	from bsa import BeautifulSoup


a__		     =     logging.get_logger(__name__)


class    SCREAMING_SNAKE_CASE_				(       __lowercase   ):



	"""simple docstring"""


	def __init__(					self		:						List[str]					,     **lowerCAmelCase		:						Optional[int]	)	->       Optional[int]:







						"""simple docstring"""







						requires_backends(self					,     ["""bs4"""]	)
						super().__init__(**lowerCAmelCase	)






	def 					lowerCamelCase__     (					self		:						Optional[int]					,     lowerCAmelCase		:						int	)	->       Tuple:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      []
						__UpperCamelCase					:	Dict             =      []
						__UpperCamelCase					:	List[str]             =      element if element.name else element.parent
						for parent in child.parents:  # type: bs4.element.Tag
											__UpperCamelCase					:	Dict             =      parent.find_all(child.name					,     recursive=lowerCAmelCase	)
											xpath_tags.append(child.name	)
											xpath_subscripts.append(
											    0 if 1 == len(lowerCAmelCase	) else next(i for i, s in enumerate(lowerCAmelCase					,     1	) if s is child	)	)
											__UpperCamelCase					:	Optional[Any]             =      parent
						xpath_tags.reverse()
						xpath_subscripts.reverse()
						return xpath_tags, xpath_subscripts






	def 					lowerCamelCase__     (					self		:						int					,     lowerCAmelCase		:						Tuple	)	->       int:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      BeautifulSoup(lowerCAmelCase					,     """html.parser"""	)

						__UpperCamelCase					:	Dict             =      []
						__UpperCamelCase					:	List[str]             =      []
						__UpperCamelCase					:	Dict             =      []

						for element in html_code.descendants:
											if type(lowerCAmelCase	) == bsa.element.NavigableString:
																if type(element.parent	) != bsa.element.Tag:
																					continue

																__UpperCamelCase					:	Optional[Any]             =      html.unescape(lowerCAmelCase	).strip()
																if not text_in_this_tag:
																					continue

																all_doc_strings.append(lowerCAmelCase	)

																__UpperCamelCase					:	str             =      self.xpath_soup(lowerCAmelCase	)
																stringaxtag_seq.append(lowerCAmelCase	)
																stringaxsubs_seq.append(lowerCAmelCase	)

						if len(lowerCAmelCase	) != len(lowerCAmelCase	):
											raise ValueError("""Number of doc strings and xtags does not correspond"""	)
						if len(lowerCAmelCase	) != len(lowerCAmelCase	):
											raise ValueError("""Number of doc strings and xsubs does not correspond"""	)

						return all_doc_strings, stringaxtag_seq, stringaxsubs_seq






	def 					lowerCamelCase__     (					self		:						str					,     lowerCAmelCase		:						Tuple					,     lowerCAmelCase		:						Any	)	->       str:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      ""
						for tagname, subs in zip(lowerCAmelCase					,     lowerCAmelCase	):
											xpath += F'''/{tagname}'''
											if subs != 0:
																xpath += F'''[{subs}]'''
						return xpath






	def __call__(					self		:						List[str]					,     lowerCAmelCase		:						Any	)	->       BatchFeature:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      False

						# Check that strings has a valid type
						if isinstance(lowerCAmelCase					,     lowerCAmelCase	):
											__UpperCamelCase					:	str             =      True
						elif isinstance(lowerCAmelCase					,     (list, tuple)	):
											if len(lowerCAmelCase	) == 0 or isinstance(html_strings[0]					,     lowerCAmelCase	):
																__UpperCamelCase					:	Dict             =      True

						if not valid_strings:
											raise ValueError(
											    """HTML strings must of type `str`, `List[str]` (batch of examples), """
											    F'''but is of type {type(lowerCAmelCase	)}.'''	)

						__UpperCamelCase					:	List[str]             =      bool(isinstance(lowerCAmelCase					,     (list, tuple)	) and (isinstance(html_strings[0]					,     lowerCAmelCase	))	)

						if not is_batched:
											__UpperCamelCase					:	Dict             =      [html_strings]

						# Get nodes + xpaths
						__UpperCamelCase					:	Union[str, Any]             =      []
						__UpperCamelCase					:	int             =      []
						for html_string in html_strings:
											__UpperCamelCase					:	Any             =      self.get_three_from_single(lowerCAmelCase	)
											nodes.append(lowerCAmelCase	)
											__UpperCamelCase					:	str             =      []
											for node, tag_list, sub_list in zip(lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase	):
																__UpperCamelCase					:	Any             =      self.construct_xpath(lowerCAmelCase					,     lowerCAmelCase	)
																xpath_strings.append(lowerCAmelCase	)
											xpaths.append(lowerCAmelCase	)

						# return as Dict
						__UpperCamelCase					:	Optional[Any]             =      {"nodes": nodes, "xpaths": xpaths}
						__UpperCamelCase					:	List[str]             =      BatchFeature(data=lowerCAmelCase					,     tensor_type=lowerCAmelCase	)

						return encoded_inputs








from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip